However, they are
transaction-oriented systems and do not address summarized information, crossreferenced
information, interactive exploration of data, and so forth. Furthermore,
they are not suited for temporal data, they are very slow to aggregate data, they
hardly deal with multiple levels of data granularity, and their user interface is too
complex for most users. Similarly to database management systems (DBMS), GIS
alone cannot fill the ???analysis gap??? between spatial data and geographic knowledge
discovery. When one has to interactively dig into data, roll them up, and cross-reference
them to get the information of interest, today??™s GIS interactivity and query
interfaces are not appropriate in terms of functions, ease-of-use, or response times.
Today??™s GIS do not support Newell??™s cognitive band of 10 seconds (Newell, 1990)
when one needs to keep his train-of-thought while analyzing spatial data.
On the other hand, even though OLAP is well-suited for knowledge discovery, it
is not adapted for the analysis of spatial data (Caron, 1998). In fact, OLAP treats
spatial data like other data and spatial analysis is limited to predefined nominal locations
(e.g., names of countries, states, regions, cities). Support for spatiotemporal
analyses is seriously limited (no spatial visualization, practically no spatial analysis,
no map-based exploration of data, etc.
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